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Document worth reading: “The Internet of Things: a Survey and Outlook”

The recent history has witnessed disruptive advances in disciplines related to information and communication technologies that have laid a rich technological ecosystem for the growth and maturity of latent paradigms…

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Document worth reading: “An introduction to domain adaptation and transfer learning”

In machine learning, if the training data is an unbiased sample of an underlying distribution, then the learned classification function will make accurate predictions for new samples. However, if the…

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If you did not already know

CollaboNet Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER)…

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Document worth reading: “On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis”

In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping on the performance of a state-of-the-art text classifier based on convolutional…

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Document worth reading: “Unit Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling: A Comprehensive Overview with Extensions”

Model-based small area estimation is frequently used in conjunction with survey data in order to establish estimates for under-sampled or unsampled geographies. These models can be specified at either the…

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Document worth reading: “Introduction to Network Theory (and Graph Theory)”

(Slide Deck) Introduction to Network Theory (and Graph Theory) Source: Data Analytics & R

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Document worth reading: “The modal age of Statistics”

Recently, a number of statistical problems have found an unexpected solution by inspecting them through a ‘modal point of view’. These include classical tasks such as clustering or regression. This…

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Document worth reading: “To Bayes or Not To Bayes That’s no longer the question!”

This paper seeks to provide a thorough account of the ubiquitous nature of the Bayesian paradigm in modern statistics, data science and artificial intelligence. Once maligned, on the one hand…

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Document worth reading: “A Survey on Bias and Fairness in Machine Learning”

With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems….

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Document worth reading: “Measure, Manifold, Learning, and Optimization: A Theory Of Neural Networks”

We present a formal measure-theoretical theory of neural networks (NN) built on probability coupling theory. Our main contributions are summarized as follows. * Built on the formalism of probability coupling…